Online research in philosophy

Accounting for phenomenal structure—the forms, aspects, and features of conscious experience—poses a deep challenge for the scientific study of consciousness, but rather than abandon hope I propose a way forward. Connectionism, I argue, offers a bi-directional analogy, with its oft-noted “neural inspiration” on the one hand, and its largely unnoticed capacity to illuminate our phenomenology on the other. Specifically, distributed representations in a recurrent network enable networks to superpose categorical, contextual, and temporal information on a specific input representation, much as our own experience does. Artificial neural networks also suggest analogues of four salient distinctions between sensory and nonsensoty consciousness. The paper concludes with speculative proposals for discharging the connectionist heuristics to leave a robust, detailed empirical theory of consciousness.

The purpose of this chapter is to outline some of the thinking behind new e-learning technology, including e-portfolios and personal learning environments. Part of this thinking is centered around the theory of connectivism, which asserts that knowledge - and therefore the learning of knowledge - is distributive, that is, not located in any given place (and therefore not 'transferred' or 'transacted' per se) but rather consists of the network of connections formed from experience and interactions with a knowing community. And another part of this thinking is centered around the new, and the newly empowered, learner, the member of the net generation, who is thinking and interacting in new ways. These trends combine to form what is sometimes called 'e-learning 2.0' - an approach to learning that is based on conversation and interaction, on sharing, creation and participation, on learning not as a separate activity, but rather, as embedded in meaningful activities such as games or workflows.

Painful stimuli evoke functional activations in the cortex, but electrical stimulation of these areas does not evoke pain sensation, nor does widespread epileptic discharge. Likewise, cortical lesions do not eliminate pain sensation. Although the cortex may contribute to pain modulation, the planning of escape responses, and learning, the network activity that constitutes the actual experience of pain probably occurs subcortically. (Published Online May 1 2007).

_role, especially in learning, and through devising hybrid neural network models that (in a qualitative manner) approxi-_ _mate characteristics of human consciousness. In doing so, the paper examines explicit and implicit learning in a variety_ _of psychological experiments and delineates the conscious/unconscious distinction in terms of the two types of learning_ _and their respective products. The distinctions are captured in a two-level action-based model C_larion_. Some funda-_ _mental theoretical issues are also clari?ed with the help of the model. Comparisons with existing models of conscious-_.

This paper examines the use of connectionism (neural networks) in modelling legal reasoning. I discuss how the implementations of neural networks have failed to account for legal theoretical perspectives on adjudication. I criticise the use of neural networks in law, not because connectionism is inherently unsuitable in law, but rather because it has been done so poorly to date. The paper reviews a number of legal theories which provide a grounding for the use of neural networks in law. It then examines some implementations undertaken in law and criticises their legal theoretical naïvete. It then presents a lessons from the implementations which researchers must bear in mind if they wish to build neural networks which are justified by legal theories.

One has to face numerous difficulties when trying to establish a dissociation between conscious and unconscious knowledge. In this paper, we review several of these problems as well as the different methodological solutions that have been proposed to address them. We suggest that each of the different methodological solutions offered refers to a different operational definition of consciousness, and present empirical examples of sequence learning studies in which these different procedures were applied to differentiate between implicit and explicit knowledge acquisition. We also show how the use of a sensitive behavioural method, the process dissociation procedure, confers a distinctive advantage in brain imaging studies when aiming to delineate the neural correlates of conscious and unconscious processes in sequence learning.

I address whether neural networks perform computations in the sense of computability theory and computer science. I explicate and defend
the following theses. (1) Many neural networks compute—they perform computations. (2) Some neural networks compute in a classical way.
Ordinary digital computers, which are very large networks of logic gates, belong in this class of neural networks. (3) Other neural networks
compute in a non-classical way. (4) Yet other neural networks do not perform computations. Brains may well fall into this last class.

The problem of inductive learning is hard, and--despite much work--no solution is in sight, from neural networks or other AI techniques. I suggest that inductive reasoning may be grounded in sensorimotor capacity. If an artificial system to generalize in ways that we find intelligent it should be appropriately embodied. This is illustrated with a network- controlled animat that learns n-parity by representing intermediate states with its own motion. Unlike other general learning devices, such as disembodied networks, it learns from very few examples and generalizes correctly to previously unseen cases.

In cognitive neuroscience, dissociating the brain networks that ing—has thus become one of the best empirical situations subtend conscious and nonconscious memories constitutes a through which to study the mechanisms of implicit learning, very complex issue, both conceptually and methodologically.

The present commentary addresses the Quartz & Sejnowski (Q&S) target article from the point of view of the dynamical learning algorithm for neural networks. These techniques implicitly adopt Q&S's neural constructivist paradigm. Their approach hence receives support from the biological and psychological evidence. Limitations of constructive learning for neural networks are discussed with an emphasis on grammar learning.